Cumulative Revelations in Personal Data takes a multidisciplinary approach to investigating how small, apparently innocuous pieces of employees' personal information, which are generated through interactions with/in networked systems over time, collectively pose significant yet unanticipated risk to personal reputation and employers' operational security. Such cumulative revelations come from personal data that are shared intentionally by an individual, from data shared about an individual by others, from recognition software that identifies and tags people and places automatically, and from common cross-authentication practices that favour convenience over security (e.g. signing into AirBnB via Facebook). Brought together, these data can provide unintended insights to others into (for example) an individual's personal habits, work patterns, personality, emotion, and social influence. Collectively these data thus have the potential to create adverse consequences for that individual (e.g. through reputational damage), their employer (e.g. by creating opportunities for cybercrime), and even for national security. The research brings together multidisciplinary expertise in Socio-Digital Interaction, Co-design, Interactive Information Retrieval, and Computational Legal Theory, all working in collaboration with a key industry partner, the Royal Bank of Scotland, which employs more than 92,000 staff across 12 national, international and private banks and for which security concerns are paramount, as well as UK Government security agencies, via the Government Office for Science and the Centre for Research and Evidence on Security Threats. The research will examine the potential adverse revelations delivered by an individual employee's holistic digital footprint through the development of a prototype software tool that maps out a portrait of a user's digital footprint and reflects it back to them. This tool will enable individuals to understand the cumulative nature of their personal data, and better comprehend the associated vulnerabilities and risks. Responding to employers' concerns over organisational security risks created by cumulative revelations of their employees' data, the research will also identify conflicts and ambiguities in security service design and implementation when the motivations and actions of individual employees are balanced against organisational security philosophy, enabling mitigation against the attendant risks, issues and consequences of cumulative revelations from organisational and individual perspectives.

Existing wireless power transfer (WPT) systems remove the inconvenience of charging portable devices, such as mobile phones, through cables, and can also help solve the problem of getting power into "hard to reach places", such as powering or recharging medical implants. However, the vast majority of existing WPT solutions are limited by being unidirectional, point-to-point systems, i.e. there is one dedicated power source, and one dedicated receiver that collects the transmitted energy. In this work, we will develop a new generation of wireless power transfer technology where we will create networks of active wireless power transceivers, allowing power to be routed, safety, around the network, over long distances and with high efficiency. The technologies that will be created in this work will enable wireless power systems to be deployed in vastly more application scenarios due to the significant increase in capability that will be created. For example: 1. The move from systems with active transmitters and passive receivers to an active-active, transceiver-transceiver approach will concurrently enable power to be moved in either direction across the magnetic link ("bidirectional wireless power transfer", as well as enabling operation of the system with lower coupling factors (due to the tuning flexibility that the active-active approach creates). Operation with lower coupling factors inherently means greater transmission distance between the power source and the receiver. 2. The creation of a network of wireless power transceivers (rather than point-to-point links), where any number of transmitters can freely join and leave the network, opens up many other new applications that would otherwise not be practical, or in some cases be possible. It will allow devices to participate, in an ad-hoc way in receiving and transmitting power into the network (as portable devices are moved around), and helping relay power from a source to a node over a number of hops, increasing the range of wireless power delivery. It will enable the efficient charging of many devices concurrently from a single transmission source, in applications such as powering a number of devices on a desk, charging many power-tools in a toolbox or case, military equipment in a soldier's backpack etc. A secondary output from this work, although one that is absolutely critical to successful deployments of wireless power in almost all application contexts, is safe operation in the prescience of people, and conducting objects (which have the potential to heat up in a similar way to induction-hob cooking). Whilst the high frequency wireless power solutions that we employ are naturally less prone to heating foreign objects, there are always scenarios where a traditional wireless power system will need to either shut off, or operate at reduced power due from a safety perspective. The use of a network of transceivers adds the possibility to route power away from and around foreign objects without having to degrade the power delivery to maintain safety, as is often required in simple point-to-point systems.

Cumulative Revelations in Personal Data takes a multidisciplinary approach to investigating how small, apparently innocuous pieces of employees' personal information, which are generated through interactions with/in networked systems over time, collectively pose significant yet unanticipated risk to personal reputation and employers' operational security. Such cumulative revelations come from personal data that are shared intentionally by an individual, from data shared about an individual by others, from recognition software that identifies and tags people and places automatically, and from common cross-authentication practices that favour convenience over security (e.g. signing into AirBnB via Facebook). Brought together, these data can provide unintended insights to others into (for example) an individual's personal habits, work patterns, personality, emotion, and social influence. Collectively these data thus have the potential to create adverse consequences for that individual (e.g. through reputational damage), their employer (e.g. by creating opportunities for cybercrime), and even for national security. The research brings together multidisciplinary expertise in Socio-Digital Interaction, Co-design, Interactive Information Retrieval, and Computational Legal Theory, all working in collaboration with a key industry partner, the Royal Bank of Scotland, which employs more than 92,000 staff across 12 national, international and private banks and for which security concerns are paramount, as well as UK Government security agencies, via the Government Office for Science and the Centre for Research and Evidence on Security Threats. The research will examine the potential adverse revelations delivered by an individual employee's holistic digital footprint through the development of a prototype software tool that maps out a portrait of a user's digital footprint and reflects it back to them. This tool will enable individuals to understand the cumulative nature of their personal data, and better comprehend the associated vulnerabilities and risks. Responding to employers' concerns over organisational security risks created by cumulative revelations of their employees' data, the research will also identify conflicts and ambiguities in security service design and implementation when the motivations and actions of individual employees are balanced against organisational security philosophy, enabling mitigation against the attendant risks, issues and consequences of cumulative revelations from organisational and individual perspectives.

With the exponentially increasing prevalence of networked sensors and other devices for collecting data in real-time, automated data analysis methods with theoretically justified performance guarantees are in constant demand. Often a key question with such streaming data is whether they show evidence of anomalous behaviour. This could, e.g., be due to malignant bot activity on a website; early warning of potential equipment failure or detection of methane leakages. These and other motivating examples share a common feature which is not accommodated by classical point anomaly models in statistics: the anomaly may not simply be an 'outlying' observation, but rather a distinctive pattern observed over consecutive observations. The strategic vision for this programme grant is to establish the statistical foundations for Detecting Anomalous Structure in Streaming data settings (DASS). Discussions with a wide-range of industrial partners from different sectors have identified important, generic challenges that cut across distinct DASS applications, and are relevant for analysing streaming data more broadly: Contemporary Constrained Environments: Anomaly detection is often performed under various constraints due, for example, to the restrictions on measurement frequency, the volume of data transferable between sensors and a central processor, or battery usage limits. Additionally, certain scenarios may impose privacy restrictions when handling sensitive data. Consequently, it has become imperative to establish the mathematical underpinning for rigorously examining the trade-offs between, e.g., statistical accuracy, communication efficiency, privacy preservation and computational demands. Handling Data Realities: A substantial portion of research in statistical anomaly detection operates under the assumption of clean data. Nevertheless, real-world data typically exhibit various imperfections, such as missing values, labelling errors in data streams, synchronisation discrepancies, sensor malfunctions and heterogeneous sensor performance. Consequently, there is a pressing need for the development of principled, model-based procedures that can effectively address the features of real data and enhance the resilience of anomaly detection methods. Identifying, Accounting for and Tracking Dependence: Not only are data streams often interdependent, but also anomalous patterns may be dependent across those streams. Taking into account both types of dependence is crucial in enhancing the statistical efficiency of anomaly detection algorithms, and also in controlling the errors arising from handling a large number of data streams in a principled way. Other challenges include tracking the path of an anomaly across multiple data sources with a view to learning causal indicators allowing for precautionary intervention. Our ambitious goal of comprehensively addressing these challenges is only achievable via the programme grant scheme. Our philosophy is to tackle the methodological, theoretical and computational aspects of these statistical problems together. This integrated approach is essential to achieving the substantive fundamental advances in statistics envisaged, and to ensuring that our new methods are sufficiently robust and efficient to be widely adopted by academics, industry and society more generally.

The way in which individuals interact with technology is rapidly evolving, as users increasingly expect fast, reliable and accurate information. In order to deliver systems capable of meeting these expectation both businesses and government departments alike are turning to conversational agents (or chatbots). These conversational agents are capable of interacting and engaging with users, answering user queries and even providing advice and guidance as required. This research considers how this technology can be optimised to provide a more effective method of communication, while also focusing on the implicit trust that a user has with a conversational agent. As part of this research we will investigate the nature of sensitive information and how the context of the information can play a role in its perceived sensitivity. This will be achieved using a range of experiments to better understand the public's perceptions of personal information, and how those perceptions relate to the classification of the information. In order to fully understand the use of conversational agents it is essential to properly understand the nature of personal, sensitive information and also their perceived trustworthiness. We will examine how different facets of a conversational agent's humanity, personality and appearance can be used to affect an individual's perceptions and trust in that agent. We will focus on the use of conversational agents across three key sectors: healthcare, defence and security and technology. These three areas have been selected as they are significant users of conversational agents and all deal with potentially sensitive and personal information, as well as being areas of significant public spending. Our research will understand how these interactions between humans and computers can be optimised to deliver a bespoke conversational agent tailored to meet the expectations and needs of the individual. This in turn will increase the trust and confidence in these digital services.